In early August, FareedKhan-dev published the kimi-k3-in-c project, which runs the Kimi K3 model with 2.78 trillion parameters on a single CPU with just 8.24 GB of RAM. This isn't another demo. It's a working inference engine in portable C99 that runs without BLAS, frameworks or a GPU. By August 27 the project had 6359 stars, 549 of them in a single day. You can find the details in the project repository on GitHub.
Until now, models this large needed expensive hardware and specialized infrastructure. Now you can:
- Deploy powerful LLMs on edge devices or in resource-constrained environments.
- Cut the operating costs of inference.
- Integrate into existing systems more easily, without rebuilding the stack.
The first thing I'll test with Kimi K3 is local agentic pipelines. When a model of this scale runs on ordinary hardware, you can run scenarios where data security and minimal latency are critical. You no longer have to choose between power and autonomy.
If GPU costs for inference have held you back, or deploying on non-standard hardware has been a headache, this news is a real find. If you're waiting for a ready-made solution in three lines of code, it's too early for that, but the direction is already clear.
Sources: https://github.com/FareedKhan-dev/kimi-k3-in-c
Video candidate (useful tip): Show kimi-k3-in-c being installed and run on a laptop, with a demo of CPU inference.